Technical Contributions Research Articles

NV-SQL: Boosting OLTP Performance with Non-Volatile DIMMs

When running OLTP workloads, relational DBMSs with flash SSDs still suffer from the durability overhead. Heavy writes to SSD not only limit the performance but also shorten the storage lifespan. To mitigate the durability overhead, this paper proposes a new database architecture, NV-SQL. NV-SQL aims at absorbing a large fraction of writes written from DRAM to SSD by introducing NVDIMM into the memory hierarchy as a durable write cache. On the new architecture, NV-SQL makes two technical contributions. First, it proposes the re-update interval-based admission policy that determines which write-hot pages qualify for being cached in NVDIMM. It is novel in that the page hotness is based solely on pages' LSN. Second, this study finds that NVDIMM-resident pages can violate the page action consistency upon crash and proposes how to detect inconsistent pages using per-page in-update flag and how to rectify them using the redo log. NV-SQL demonstrates how the ARIES-like logging and recovery techniques can be elegantly extended to support the caching and recovery for NVDIMM data. Additionally, by placing write-intensive redo buffer and DWB in NVDIMM, NV-SQL eliminates the log-force-at-commit and WAL protocols and further halves the writes to the storage. Our NV-SQL prototype running with a real NVDIMM device outperforms the same-priced vanilla MySQL with larger DRAM by several folds in terms of transaction throughput for write-intensive OLTP benchmarks. This confirms that NV-SQL is a cost-performance efficient solution to the durability problem.

Rethinking Modern Communication from Semantic Coding to Semantic Communication

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL)-powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges such as the information overhead, semantic security and implementation cost are also discussed and envisioned.

Gated Adversarial Network Based Environmental Enhancement Method for Driving Safety Under Adverse Weather Conditions

The adverse weather conditions have brought considerable difficulties in vision-based applications, which are closely related to the driving safety of autonomous vehicles. However, to date, the greater part of the existing environmental perception studies are under ordinary conditions, and the method to deal with the adverse weather conditions was ignored. Hence, this paper proposes an all-in-one gated adversarial network (AIO-GAN) to improve the performance of vision-based environment perception algorithms under adverse weather conditions, including in rain, haze, lack of light, etc. Three key technical contributions are made. At first, the deep learning based gated transformer module was proposed to classify the input mixed images to different collections by passing them through different branches. Second, the multi-branch based variational autoencoder-generative adversarial network was proposed to solve the ill-pose problem of the solution. Third, high-level weight sharing encoders was given out to guarantee the stability and the high quality of the training process. In this way, the unified clean-style images can be achieved, even if the mixed multi-modal images are transferred from the source domain of complex weather scenes. Extensive experimental results show that the proposed method has achieved better performance than state-of-the-arts and improved the accuracy of target detection.

Optimal Active Power Generation in Electrical Networks Using Distributed Fixed-Time Control of Multi-Agent Systems

This paper tackles the Economic Dispatch Problem (EDP) in power systems, with the objective of generating optimal active power at each source to minimize overall generation costs, while maintaining the power generation-demand equality and staying within the source generation limits. The main motivation behind this work is to create a power optimization strategy that accomplishes these goals while providing superior performance in terms of convergence speed and accuracy of the optimal solution. To this end, a fixed-time active power optimization protocol is proposed for power sources in electrical networks. Distributed and local fixed-time control laws of first-order multi-agent systems are developed for this purpose. This paper presents four technical contributions: i) minimizing active power generation costs in electrical network sources within a predetermined maximum settling time without violating their generation limits while ensuring generation-demand equality, ii) a distributed overall control system connected through a sparse communication network modeled as an undirected graph that avoids the pitfalls of centralized solutions, iii) a model-free optimization method that does not require previous knowledge of power system parameters, and iv) a continuous optimization law that provides smooth power variations. Simulations are conducted to demonstrate the effectiveness of the proposed approach.

Multipartite Intrinsic Non-Locality and Device-Independent Conference Key Agreement

In this work, we introduce multipartite intrinsic non-locality as a method for quantifying resources in the multipartite scenario of device-independent (DI) conference key agreement. We prove that multipartite intrinsic non-locality is additive, convex, and monotone under a class of free operations called local operations and common randomness. As one of our technical contributions, we establish a chain rule for two variants of multipartite mutual information, which we then use to prove that multipartite intrinsic non-locality is additive. This chain rule may be of independent interest in other contexts. All of these properties of multipartite intrinsic non-locality are helpful in establishing the main result of our paper: multipartite intrinsic non-locality is an upper bound on secret key rate in the general multipartite scenario of DI conference key agreement. We discuss various examples of DI conference key protocols and compare our upper bounds for these protocols with known lower bounds. Finally, we calculate upper bounds on recent experimental realizations of DI quantum key distribution.

Rapid Design Optimization and Calibration of Microwave Sensors Based on Equivalent Complementary Resonators for High Sensitivity and Low Fabrication Tolerance

This paper presents the design, optimization, and calibration of multivariable resonators for microwave dielectric sensors. An optimization technique for the circular complementary split ring resonator (CC-SRR) and square complementary split ring resonator (SC-SRR) is presented to achieve the required transmission response in a precise manner. The optimized resonators are manufactured using a standard photolithographic technique and measured for fabrication tolerance. The fabricated sensor is presented for the high-resolution characterization of dielectric substrates and oil samples. A three-dimensional dielectric container is attached to the sensor and acts as a pool for the sample under test (SUT). In the presented technique, the dielectric substrates and oil samples can interact directly with the electromagnetic (EM) field emitted from the resonator. For the sake of sensor calibration, a relation between the relative permittivity of the dielectric samples and the resonant frequency of the sensor is established in the form of an inverse regression model. Comparisons with state-of-the-art sensors indicate the superiority of the presented design in terms of oil characterization reliability. The significant technical contributions of this work include the employment of the rigorous optimization of geometry parameters of the sensor, leading to its superior performance, and the development and application of the inverse-model-based calibration procedure.

Probabilistic Resource-Aware Session Types

Session types guarantee that message-passing processes adhere to predefined communication protocols. Prior work on session types has focused on deterministic languages but many message-passing systems, such as Markov chains and randomized distributed algorithms, are probabilistic. To implement and analyze such systems, this article develops the meta theory of probabilistic session types with an application focus on automatic expected resource analysis. Probabilistic session types describe probability distributions over messages and are a conservative extension of intuitionistic (binary) session types. To send on a probabilistic channel, processes have to utilize internal randomness from a probabilistic branching or external randomness from receiving on a probabilistic channel. The analysis for expected resource bounds is smoothly integrated with the type system and is a variant of automatic amortized resource analysis. Type inference relies on linear constraint solving to automatically derive symbolic bounds for various cost metrics. The technical contributions include the meta theory that is based on a novel nested multiverse semantics and a type-reconstruction algorithm that allows flexible mixing of different sources of randomness without burdening the programmer with complex type annotations. The type system has been implemented in the language NomosPro with linear-time type checking. Experiments demonstrate that NomosPro is applicable in different domains such as cost analysis of randomized distributed algorithms, analysis of Markov chains, probabilistic analysis of amortized data structures and digital contracts. NomosPro is also shown to be scalable by (i) implementing two broadcast and a bounded retransmission protocol where messages are dropped with a fixed probability, and (ii) verifying the limiting distribution of a Markov chain with 64 states and 420 transitions.

Keynes’ denial of conflict: Why The General Theory is a misleading guide to capitalism and stagnation

Keynes’ General Theory was a huge step forward relative to classical economics, but it was also a step backward in its denial of the conflictual nature of capitalism. There is need to understand Keynes’ technical contributions regarding the workings of monetary economies, but also need to understand the flaws within his thinking and the consequences thereof. Keynes made a fundamental contribution elucidating the mechanism of effective demand, and he also has claim to be the preeminent monetary theorist. However, his critique of classical economics was technocratic and focused on the interest rate mechanism. Reflecting his own liberal political economic disposition, he denied the conflictual nature of capitalism. That is the “original sin” in Keynesian economics, and it has far-reaching implications. It explains why establishment Keynesian economics struggles to explain the current tendency to stagnation. Even more importantly, it has kept economics locked into a false conception of capitalism that undermines the case for Social Democracy.

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

This paper presents a new user experience for online apartment search using functionality and comfort as query items. Specifically, it has three technical contributions. First, we present a new dataset on the perceived functionality and comfort scores of residential floor plans using nine question statements about the level of comfort, openness, privacy, etc. Second, we propose an algorithm to predict the scores from the floor plan images. Lastly, we implement a new apartment search system and conduct a large-scale usability study using crowdsourcing. The experimental results show that our apartment search system can provide a better user experience. To the best of our knowledge, this is the first work to propose a highly accurate machine learning model for predicting the subjective functionality and comfort of apartments.

Green IoT Protection: Sustainability-Driven Machine Intelligence for Malware Defense

As the Internet of Things (IoT) continues to expand, the security of connected devices becomes a paramount concern. Malicious actors exploit vulnerabilities in these devices, leading to severe consequences such as data breaches, privacy infringements, and service disruptions. Traditional security measures struggle to keep pace with the evolving threat landscape, necessitating advanced solutions. In this paper, we present a pioneering approach to fortify the security of IoT environments against malware through the integration of advanced machine intelligence techniques. Our work addresses this critical concern by introducing a comprehensive Machine Intelligence Strategy designed to detect and classify malware in IoT ecosystem. Leveraging Support Vector Machines (SVM) with different kernel choices, our strategy offers a multi-faceted defense mechanism. Through extensive experimentation and evaluation on public dataset of malware images, we demonstrate the efficacy of our strategy in fortifying the guardianship of connected devices, fostering a safer and more resilient IoT ecosystem. Beyond technical contributions, our research fosters a deeper understanding of the symbiotic relationship between machine intelligence and IoT security, propelling advancements in safeguarding the ever-expanding landscape of interconnected devices.

Predicting the Intention to Use the Investment Aggregate Functionality in the Context of Open Banking Using the Artificial Neural Network Approach

This study investigates the determining factors influencing the intention to use investment aggregator functionality in the context of Open Banking. A survey involving 167 participants was conducted, employing an in-depth analysis through Artificial Neural Networks (ANN). The findings reveal that the construct of "innovativeness" is the most influential factor for individuals’ intention to utilize investment aggregator functionality. Furthermore, “trust,” “social influence,” “optimism,” and “perceived usefulness” were identified as additional significant factors. Notably, individuals already engaged in investment activities, including variable income or fixed income investments, exhibited a higher inclination to adopt technology. These findings contribute to companies and banks considering the implementation of Investment Aggregator technology by providing valuable insights for effective adoption strategies. Additionally, this study offers technical contributions to Open Banking technology and aggregating tools, expanding knowledge in the field and presenting practical recommendations for companies to successfully apply this technology. Moreover, the study contributes to the deepening of the theme and the advancement of knowledge in non-linear methods, enriching the understanding of this evolving field.

Theme Transformer: Symbolic Music Generation With Theme-Conditioned Transformer

Attention-based Transformer models have been increasingly employed for automatic music generation. To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a priming sequence and ask a Transformer decoder to generate a continuation. However, this <i>prompt-based</i> conditioning cannot guarantee that the conditioning sequence would develop or even simply repeat itself in the generated continuation. In this paper, we propose an alternative conditioning approach, called <i>theme-based</i> conditioning, that explicitly trains the Transformer to treat the conditioning sequence as a thematic material that has to manifest itself multiple times in its generation result. This is achieved with two main technical contributions. First, we propose a deep learning-based approach that uses contrastive representation learning and clustering to automatically retrieve thematic materials from music pieces in the training data. Second, we propose a novel gated parallel attention module to be used in a sequence-to-sequence (seq2seq) encoder/decoder architecture to more effectively account for a given conditioning thematic material in the generation process of the Transformer decoder. We report on objective and subjective evaluations of variants of the proposed Theme Transformer and the conventional prompt-based baseline, showing that our best model can generate, to some extent, polyphonic pop piano music with repetition and plausible variations of a given condition.

Brute Force Attack on Distributed data Hiding in the Multi-Cloud Storage Environment More Diminutive than the Exponential Computations

Classical steganography is designed to hide data by cover media. Recent approaches fragmented the data and hide them in a distributed manner by embedding each fragmented data into a distinct cover media. This approach makes a secret message extremely difficult for an attacker to detect. However, cover media modification leaves fingerprints that could expose a secret channel to an attacker. To overcome the problem, a novel steganography concept designed by two technical contributions. I). cover media does not undergo any modification, i.e., the cover media act as a pointer to fragmented data. II). A secret message is stored in the multi-cloud storage environment. The approach claimed that it is computationally infeasible for an attacker to detect and extract the hidden message despite of having fully access to the accounts of the different clouds. In this paper, we analysed the security strength of the novel steganography concept and concluded that, attacker can get the secret value stored in multi-cloud storage environment using the brute force attacks more diminutive than exponential computations.

Evacuating from ℓp unit disks in the wireless model

The search-type problem of evacuating 2 robots in the wireless model from the (Euclidean) unit disk was first introduced and studied by Czyzowicz et al. (2014) [13]. Since then, the problem has seen a long list of follow-up results pertaining to variations as well as to upper and lower bound improvements. All established results in the area study this 2-dimensional search-type problem in the Euclidean metric space where the search space, i.e. the unit disk, enjoys significant (metric) symmetries.We initiate and study the problem of evacuating 2 robots in the wireless model from ℓp unit disks, p∈[1,∞), where in particular robots' moves are measured in the underlying metric space. To the best of our knowledge, this is the first study of a search-type problem with mobile agents in more general metric spaces. The problem is particularly challenging since even the circumference of the ℓp unit disks have been the subject of technical studies. In our main result, and after identifying and utilizing the very few symmetries of ℓp unit disks, we design optimal evacuation algorithms that vary with p. Our main technical contributions are two-fold. First, in our upper bound results, we provide (nearly) closed formulae for the worst-case cost of our algorithms. Second, and most importantly, our lower bounds' arguments reduce to a novel observation in convex geometry which analyzes trade-offs between arc and chord lengths of ℓp unit circles as the endpoints of the arcs (chords) change position around the perimeter of the disk, which we believe is interesting in its own right. Part of our argument pertaining to the latter property relies on a computer assisted numerical verification that can be done for non-extreme values of p.

A guideline for the appropriate recognition of shared resource laboratories in publication.

The issue of what level of contribution warrants authorship, determining a fair order of authors and when and whom to acknowledge in publications is often a cause of debate, and in some instances, has also been a focus of conflict at certain institutions. Shared resource laboratories (SRLs) play a fundamental role in supporting publications, and SRL staff scientists can contribute to numerous areas such as experimental design, sample preparation, data acquisition, data analysis and manuscript drafting and review. However, SRL staff scientists are often unfairly omitted from the author list. To avoid SRLs and SRL staff scientist contributions going unnoticed, the authors have formulated a set of guidelines to aid in the conceptualization and recognition of the technical and intellectual contributions of SRLs. As a better understanding of the role SRL staff scientists play in the achievement of the scientific lead's experimental aims will foster a positive feedback loop, where acknowledgements can lead to more support and funding for SRLs and more engaged SRL staff capable of supporting discoveries and technological innovations that underpin major advancements in the field of life sciences.

Kidney tracking for live augmented reality in stereoscopic mini-invasive partial nephrectomy

ABSTRACT Mini-invasive partial nephrectomy (MIPN) has many advantages. However, it is challenging for the surgeon to localise the hidden anatomical structures to be spared or resected during surgery. Augmented reality (AR) is a promising localisation assistance approach. Existing AR-MIPN methods augment the endoscopic view with 3D models from preoperative CT scan. However, they do not track the kidney in real-time, which considerably reduces usability, as AR is only temporarily available on isolated images. We propose an approach to achieve continuous live AR-MIPN. It uses classical camera calibration and manual initial registration. Its key novelty is a keypoint-based automatic kidney tracking module, with three main technical contributions. First, it performs stereo tracking-by-detection from stereo keyframes, exploiting left-right consistency to maximise robustness. Second, it only considers keypoints within the parenchyma, as segmented by a specifically trained neural network. Third, it improves keypoint detection and matching by a new process that we call stereo perspective correction (SPC). It uses the stereo depth-map and surface flattening to generate an image warp that cancels the perspective effect, improving the performance of keypoint detection and matching. We carried out experiments on semi-synthetic and real surgical datasets to compare several tracking methods, showing that our method outperforms.

Asymptotic State Transformations of Continuous Variable Resources

We study asymptotic state transformations in continuous variable quantum resource theories. In particular, we prove that monotones displaying lower semicontinuity and strong superadditivity can be used to bound asymptotic transformation rates in these settings. This removes the need for asymptotic continuity, which cannot be defined in the traditional sense for infinite-dimensional systems. We consider three applications, to the resource theories of (I) optical nonclassicality, (II) entanglement, and (III) quantum thermodynamics. In cases (II) and (III), the employed monotones are the (infinite-dimensional) squashed entanglement and the free energy, respectively. For case (I), we consider the measured relative entropy of nonclassicality and prove it to be lower semicontinuous and strongly superadditive. One of our main technical contributions, and a key tool to establish these results, is a handy variational expression for the measured relative entropy of nonclassicality. Our technique then yields computable upper bounds on asymptotic transformation rates, including those achievable under linear optical elements. We also prove a number of results which guarantee that the measured relative entropy of nonclassicality is bounded on any physically meaningful state and easily computable for some classes of states of interest, e.g., Fock diagonal states. We conclude by applying our findings to the problem of cat state manipulation and noisy Fock state purification.

PP95 Health Professionals’ Participation On Health Technology Assessment (HTA) Public Consultation: A Distribution Analysis Of Brazilian HTA In 2021

IntroductionThe Brazilian HTA process includes calls for public consultations, in which society can give its opinion on reports emitted by the National Committee for Health Technology Incorporation (CONITEC). Open and closed queries for public consultation are performed by official formularies and are available online at CONITEC webpage. There are two categories of queries: clinical protocols and guidelines, and incorporation/exclusion demands. Incorporation/exclusion queries are subdivided in two additional categories: opinion and experience, or technical. In this study we analyze health professionals’ technical contributions and their opinion (pro or con) on the inclusion or exclusion of health technologies.MethodsOn November 26th, 2021, formularies concerning concluded public consultations on health technology incorporation/exclusion reports were extracted from CONITEC website for the period, January 1, 2021 to November 26, 2021. Entries on the technical contributions formularies included a close-ended question about the opinion of participants on health technology incorporation/exclusion reports (“favorable”/“against”/“neither”).ResultsA total of 63 health technology incorporation/exclusion queries were carried out during the study period, of which only 4 were exclusions. A total of 7783 contributions were registered. “Patients”, “Family or caregivers”, “Interest on the theme”, and “Health professionals”, accounted for 96.4% (10.9, 15.2, 17.1 and 53% respectively). Health professionals’ participation alone accounted for 4130 entries. Concerning the category “health professionals”, the total number of favorable opinions on the presented documents was 2740 (66.3%), 1306 (31.6%) disagreed, and 84 (2%) had no opinion.ConclusionsHealth professionals can be considered one of the main stakeholders considering HTA for technology incorporation in public health systems. Brazilian HTA reports are submitted to public consultation through queries, which are available open access at the Brazilian National Committee for Health Technology Incorporation website.

Super-Resolution TOA and AOA Estimation for OFDM Radar Systems Based on Compressed Sensing

This article presents a compressed sensing-based time of arrival (TOA) and angle-of-arrival (AOA) estimation algorithm for orthogonal frequency division multiplexing (OFDM) radar systems. The algorithm is designed for noncooperative targets based on a uniform linear array using a cyclic prefix (CP) added OFDM signal. The algorithm makes three key technical contributions. First, the algorithm adopts the CP-based OFDM signal for the radar TOA/AOA estimation to suppress the multitarget interference and the impact of time delay on the subcarrier orthogonality. Second, this article exploits the structure of the CP-OFDM radar signal model to construct the optimization problem of the joint TOA/AOA recovery. The super-resolution AOA estimation is obtained by using a redundant dictionary containing much more basis than the number of antennas. Third, the algorithm proposes an efficient way to solve the optimization by utilizing the properties of the circulant matrix, fast Fourier transform, and Hadamard multiplication. The simulation results indicate the effectiveness of the proposed algorithm.

QuadStream

Streaming rendered 3D content over a network to a thin client device, such as a phone or a VR/AR headset, brings high-fidelity graphics to platforms where it would not normally possible due to thermal, power, or cost constraints. Streamed 3D content must be transmitted with a representation that is both robust to latency and potential network dropouts. Transmitting a video stream and reprojecting to correct for changing viewpoints fails in the presence of disocclusion events; streaming scene geometry and performing high-quality rendering on the client is not possible on limited-power mobile GPUs. To balance the competing goals of disocclusion robustness and minimal client workload, we introduce QuadStream , a new streaming content representation that reduces motion-to-photon latency by allowing clients to efficiently render novel views without artifacts caused by disocclusion events. Motivated by traditional macroblock approaches to video codec design, we decompose the scene seen from positions in a view cell into a series of quad proxies , or view-aligned quads from multiple views. By operating on a rasterized G-Buffer, our approach is independent of the representation used for the scene itself; the resulting QuadStream is an approximate geometric representation of the scene that can be reconstructed by a thin client to render both the current view and nearby adjacent views. Our technical contributions are an efficient parallel quad generation, merging, and packing strategy for proxy views covering potential client movement in a scene; a packing and encoding strategy that allows masked quads with depth information to be transmitted as a frame-coherent stream; and an efficient rendering approach for rendering our QuadStream representation into entirely novel views on thin clients. We show that our approach achieves superior quality compared both to video data streaming methods, and to geometry-based streaming.